From c2b0f7e4fb4738afcae1705db8f983dd90a669a4 Mon Sep 17 00:00:00 2001
From: historia <historiavg@proton.me>
Date: Sun, 6 Sep 2026 15:17:57 -0400
Subject: inital commit

---
 lib/tests/conftest.py | 70 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 lib/tests/conftest.py

(limited to 'lib/tests/conftest.py')

diff --git a/lib/tests/conftest.py b/lib/tests/conftest.py
new file mode 100644
index 0000000..885d22b
--- /dev/null
+++ b/lib/tests/conftest.py
@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+SRC = Path(__file__).resolve().parents[1] / "src"
+sys.path.insert(0, str(SRC))
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+SR = 44100
+
+
+def speechish(
+    dur: float,
+    sr: int = SR,
+    level_dbfs: float = -20.0,
+    seed: int = 0,
+) -> np.ndarray:
+    rng = np.random.default_rng(seed)
+    n = int(sr * dur)
+    t = np.arange(n) / sr
+    f0 = 110.0 * (1.0 + 0.02 * np.sin(2 * np.pi * 0.9 * t))
+    phase = 2 * np.pi * np.cumsum(f0) / sr
+    x = np.zeros(n)
+    for k in range(1, 9):
+        x += (1.0 / k**1.3) * np.sin(k * phase + 0.3 * k)
+    syll = 0.5 + 0.5 * np.sin(2 * np.pi * 3.0 * t + float(rng.uniform(0, 6)))
+    pauses = (np.sin(2 * np.pi * 0.5 * t) > -0.6).astype(float)
+    env = np.clip(syll, 0.02, 1.0) ** 0.6 * np.maximum(pauses, 0.05)
+    x = x * env
+    x /= np.max(np.abs(x)) + 1e-12
+    return (x * (10 ** (level_dbfs / 20.0))).astype(np.float32)
+
+
+def sine(freq: float, dur: float, sr: int = SR, peak_dbfs: float = -20.0) -> np.ndarray:
+    t = np.arange(int(sr * dur)) / sr
+    return (10 ** (peak_dbfs / 20.0) * np.sin(2 * np.pi * freq * t)).astype(np.float32)
+
+
+def band_db(x: np.ndarray, sr: int, lo: float, hi: float) -> float:
+    from scipy import signal
+
+    sos = signal.butter(4, [lo, hi], btype="bandpass", fs=sr, output="sos")
+    y = signal.sosfilt(sos, x.astype(np.float64))
+    r = np.sqrt(np.mean(np.square(y)))
+    if r <= 0:
+        return -120.0
+    return float(20 * np.log10(r))
+
+
+@pytest.fixture
+def sr() -> int:
+    return SR
+
+
+@pytest.fixture
+def speech() -> np.ndarray:
+    return speechish(6.0, level_dbfs=-20.0)
+
+
+@pytest.fixture
+def noisy_speech(sr, speech) -> np.ndarray:
+    rng = np.random.default_rng(7)
+    noise = rng.standard_normal(speech.size)
+    noise *= (10 ** (-48.0 / 20.0)) / np.sqrt(np.mean(np.square(noise)))
+    hum = 0.003 * np.sin(2 * np.pi * 50.0 * np.arange(speech.size) / sr)
+    return (speech + noise + hum).astype(np.float32)
-- 
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